Analisis dan Perancangan Sistem E-Marketing pada PT. Nordic Lift Truck

The purpose of research is to analyze the current marketing system at PT.Nordick Lift Truck in order to identify the existing problems faced by the company and to identify its weaknesses. Additionally, this research designs a web based Marketing system. The system has been designed to give facilitation for customer to get information abaut material of tutorial activity fast and efficient. The methodologies used are literature study, observation, interview, questionaire, analysis and design with 7-stages E-Marketing. It isconcluded that e-CRM system that has been designed supports information access easily and fast. The e-Marketing application consists of features such as Online Register, Contact us, Download, News, and Complaint. It is expected to help deliver and maintain customer satisfaction by building a close relationship to the customers

Kaplan–Meier survival curves.

<p>The p-values indicate that the difference between the low–risk group (green) and the high–risk group (red) is statistically significant. Using all the three modules, which are associated with the interferon pathway, mitotic cell cycle, and outliers; results in a better p-value (<b>a</b>) compared to a model without the interferon pathway (<b>b</b>). The orange horizontal lines indicate that both models have similar accuracies. However, including the interferon pathway improves the p-value, because more samples are classified in total (i.e., 38 low–risk plus 54 high–risk cases in (a), compared to 40 low–risk plus 35 high–risk cases in (b)).</p

The distribution of melanoma Clark stages in each risk group.

Also, the percentage of each stage class in each risk group is shown. The low–risk group is enriched in patients at stages III and IV. The high–risk group is enriched in patients at stages IV and V.</p

Additional file 2 of Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

Table S2. Overrepresented pathways. The canonical pathways that are overrepresented in the modules are available as part of the online supplementary materials. Each sheet in the excel file corresponds to a module. The statistics for each pathway (gene set) is reported on one row, in particular, the p-value of a hypergeometric test with the null hypothesis that the genes from the pathway were observed in the module by chance. The other columns include the name of the pathway (Set Name), the number of genes observed in the module (In Module), the number of the genes from the pathway that are present in our pool of 9,166 genes (Set Size), percentage of those genes that are in the module (% In Module), the name of the collection in MSIGDB [83] (Source), and a link to more information on the pathway (Description). There was no statistically significant pathway with p-value less than 10−4 for the modules that are not included. (XLS 131 kb

Additional file 9 of Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

Supplementary Data 1. DNA-methylation. The DNA methylation of MMP9, MMP8, and MMP25 genes in TCGA dataset are available as part of the online supplementary materials. The two sheets include the patient barcodes for 194 AML and 368 control samples. The β values were reported at cg04656101 (equivalent to chr20:44,645,014 in hg19), cg01092036 (chr11:102,595), and cg02680314 (chr16:3,097,388), respectively (Additional file 1: Figure S9b). (XLS 102 kb

Additional file 11 of Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

Table S6. The cell cycle and translational control modules. Two modules with 319 and 193 genes were automatically selected by breast cancer survival analysis. In each sheet, gene symbols, Entrez IDs, and weights in the corresponding modules are reported. Genes are sorted based on their weights. (XLS 55 kb

Simulation results.

<p>The figure plots the mean (A) genotype error and (B) clone frequency error as a function of the number of subsections. Each mean is computed over 100 simulated data sets. For each data set, the EM optimization is repeated from 10 different random initializations, and the results corresponding to the largest log likelihood are reported.</p

Inferring Clonal Composition from Multiple Sections of a Breast Cancer

<div><p>Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.</p></div

Additional file 10 of Differential protein expression in the hippocampi of resilient individuals identified by digital spatial profiling

Additional file 10: Differential expression of proteins when comparing NFT-bearing neurons of all Braak stage IV cases to NFTs of all Braak stage VI case

Additional file 1 of Differential protein expression in the hippocampi of resilient individuals identified by digital spatial profiling

Additional file 1: DSP antibody probes analyzed (86 proteins